The variational quantum circuit (VQC, also known as parameterised quantum circuit, PQC) approach, first proposed for solving the ground state energy of molecules [1]}, have been extended to many open research problems including in the field of quantum machine learning [2]}, quantum chemistry [3]}, option pricing [4]} and quantum error correction [5]}, [6]}. The performance of VQC methods largely depend on the choice of a suitable ansätze, which is not an easy task because generally the search space is very large and it is not well established whether there is a common principle for designing such ansätze. For problems involving physical systems such as in quantum chemistry, we can rely on the well-defined properties of molecular systems for ansätz designing, like the hardware efficient ansätze [7]} and physical-inspired ansätze, such as \(k\) -UpCCGSD  [8]}. However, this cannot be generalised to other areas such as designing variational error correction circuits or quantum optimisation problems. For example, in [6]}, when developing a variational circuit that can encode logical states for the 5-qubit quantum error correction code, the authors adopted an expensive approach by randomly searching over a large number (order of 10000) of circuits. It is anticipated that, with the increasing number of application areas for VQCs and the need for scalability to tackle large problem sizes without relying on fundamental physical properties, such random search methods or methods based purely on human heuristics will struggle to find suitable ansätzes. Therefore, it is important to develop efficient methods for the automated design of variational quantum circuits. Here we focus on the development of algorithms for the automated design of VQCs by leveraging the power of artificial intelligence (AI) which can be deployed for a wide range of applications.
